






from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
      from .features import FeatureType







@dataclass
class 				UpperCAmelCase_  :
     __lowerCamelCase      				=  42
     __lowerCamelCase      				=  None
     # Automatically constructed
     __lowerCamelCase      				=  "dict"
     __lowerCamelCase      				=  None
     __lowerCamelCase      				=  field(default='Translation'							,      init=__lowerCamelCase							,      repr=__lowerCamelCase		)




     def __call__(				self		):
          return pa.struct({lang: pa.string() for lang in sorted(self.languages		)}		)




     def   __UpperCAmelCase  (				self		):
          from .features import Value

          return {k: Value("""string"""		) for k in sorted(self.languages		)}







@dataclass
class 				UpperCAmelCase_  :
     __lowerCamelCase      				=  None
     __lowerCamelCase      				=  None
     __lowerCamelCase      				=  None
     # Automatically constructed
     __lowerCamelCase      				=  "dict"
     __lowerCamelCase      				=  None
     __lowerCamelCase      				=  field(default='TranslationVariableLanguages'							,      init=__lowerCamelCase							,      repr=__lowerCamelCase		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Tuple				      =			sorted(set(self.languages		)		) if self.languages else None
          UpperCAmelCase__      :  str				      =			len(self.languages		) if self.languages else None




     def __call__(				self		):
          return pa.struct({"""language""": pa.list_(pa.string()		), """translation""": pa.list_(pa.string()		)}		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  int				      =			set(self.languages		)
          if self.languages and set(_lowerCAmelCase		) - lang_set:
               raise ValueError(
                   f"Some languages in example ({', '.join(sorted(set(_lowerCAmelCase		) - lang_set		)		)}) are not in valid set ({', '.join(_lowerCAmelCase		)})."		)

          # Convert dictionary into tuples, splitting out cases where there are
          # multiple translations for a single language.
          UpperCAmelCase__      :  str				      =			[]
          for lang, text in translation_dict.items():
               if isinstance(_lowerCAmelCase	,						_lowerCAmelCase		):
                    translation_tuples.append((lang, text)		)
               else:
                    translation_tuples.extend([(lang, el) for el in text]		)

        # Ensure translations are in ascending order by language code.
          UpperCAmelCase__      ,		UpperCAmelCase__      :  int				      =			zip(*sorted(_lowerCAmelCase		)		)

          return {"language": languages, "translation": translations}




     def   __UpperCAmelCase  (				self		):
          from .features import Sequence, Value

          return {
              "language": Sequence(Value("""string"""		)		),
              "translation": Sequence(Value("""string"""		)		),
          }







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__  =					{
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					['''DebertaTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''DebertaForMaskedLM''',
       '''DebertaForQuestionAnswering''',
       '''DebertaForSequenceClassification''',
       '''DebertaForTokenClassification''',
       '''DebertaModel''',
       '''DebertaPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFDebertaForMaskedLM''',
       '''TFDebertaForQuestionAnswering''',
       '''TFDebertaForSequenceClassification''',
       '''TFDebertaForTokenClassification''',
       '''TFDebertaModel''',
       '''TFDebertaPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
   from .tokenization_deberta import DebertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_deberta_fast import DebertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deberta import (
          DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          DebertaForMaskedLM,
          DebertaForQuestionAnswering,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaModel,
          DebertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_deberta import (
          TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
          TFDebertaModel,
          TFDebertaPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




